Artificial intelligence (AI) and machine learning (ML) offer companies tremendous potential to improve profitability by optimizing several areas of their operations. Real-time data from AI/ML models helps organizations enhance marketing, demand forecasting, supply chain, and information technology. Leveraging data provides critical decision support, empowering managers to make timely, cost-saving course corrections in these areas.
AI/ML models enable companies to segment their customer bases and send relevant marketing promotions to their established targets. Higher promotion relevance yields better-qualified sales leads and reduces marketing program waste and costs. By collecting detailed information about customers’ previous purchases and engagements with promotions, AI/ML models can inform future customer-specific marketing promotions. For example, a car manufacturer with access to a prospective customer’s online search history might discover a heavy search emphasis on electric vehicle (EV) information. The manufacturer might opt to send only EV-specific promotions to this prospective customer.
AI/ML models can inform promotions at both the campaign and individual levels. One company uses AI models to design promotion campaigns around variables such as market conditions, consumer behavior, historical performance, seasonality, channel dynamics, and the competitive landscape. The organization adjusts its campaigns’ promotion mechanisms, consumer segments, discount levels, and frequency. Its AI-based approach has yielded 5 to 20 percent improvements in promotion-generated sales and profit margins.
Consumers prefer to receive relevant promotions, especially when they receive scores of marketing messages every day. The more relevant these messages are, the stronger the potential for building brand loyalty. Retail commerce platform provider Shopify reports that 70 percent of consumers want retailers to know about their buying preferences. One of Shopify’s retail clients used an ML tool to personalize content, timing, and channel selection for individual customers in its campaigns. The result was a 42 percent higher returning customer rate and 91 percent year-over-year growth in international revenue, with 50 percent lower campaign administration costs.
Real-Time Demand Forecast Adjustments
Companies using AI/ML to gain insight into consumer preferences and buying patterns can forecast demand for their products with reasonable accuracy. This lets them optimize their production and distribution operations—and potentially cut costs substantially.
ML algorithms provide real-time demand data, enabling companies to continuously adjust their forecasts in real time. Advancements in algorithms, such as the integration of natural language processing (NLP) and image recognition, facilitate the analysis of unstructured data. These data include customer reviews and social media posts, which indicate consumer sentiment. NLP and image recognition supplement the capabilities of commonly used neural networks and linear regression, identifying complex patterns within large datasets.
The real-time data-processing capability of AI/ML algorithms offers a significant advantage over traditional forecasting methods that rely on historical data aggregated over weeks, months, or buying seasons. Companies can use real-time data to inform rapid forecast adjustments amid market changes and external factors. Additionally, when companies share AI/ML inventory data with their suppliers, both can optimize their inventories.
Seasonal buying patterns significantly impact some organizations’ profitability. AI/ML adds additional value to demand forecasting for companies that generate seasonally disproportionate revenue or offer seasonal products. Suppose such a company fails to forecast seasonal or holiday demand accurately. In that case, its customers might face shortages of popular items, shipping delays, or higher prices due to increased demand. Specifically, ML models can analyze seasonal shopping habits and identify seasonally popular products and customer buying patterns. The company can use AI/ML data to plan its production and distribution accordingly and prevent such shocks for customers, which can degrade brand loyalty and sales.
Supply Chain Optimization: Inventory and Delivery
Key supply chain decisions largely depend on accurate demand forecasting, perhaps most notably with raw materials and finished product inventory levels. Generative AI/ML models can employ advanced algorithms to optimize stock levels based on demand forecasts and other variables such as lead times and market trends. Some models are probabilistic and simulate different demand scenarios based on these variables. These models use predictive analytics to help supply chain managers determine what to order and when, reducing the risk of stockouts.
One of the most impactful optimization applications for AI/ML models is product distribution. AI/ML-enabled delivery route optimization systems use continuous inputs from various sources, including GPS traffic updates, weather forecasts, and current delivery truck locations. The systems generate and adjust routes in real time based on these data. For example, United Parcel Service (UPS) uses an On-Road Integrated Optimization and Navigation (ORION) system to analyze its delivery routes, reducing delivery miles by several million and saving the company millions of gallons of fuel annually.
Richer Data Enhances Decision Support
Operations executives concerned with optimizing raw material and product inventories to handle demand fluctuations want to augment data center resources and sustain the information systems. Currently, virtual machines (VM), which are easily migrated from one server to another, account for an estimated 40 percent of data center usage. VMs often burden physical host machines (HM), leading to a decline in VM performance. A study investigated the use of a hybrid ML model integrating multiple algorithms to optimize VM migration to target HMs. The hybrid model achieved 99 percent migration accuracy, indicating that AI/ML can support information technology optimization.
Using data analysis to support critical operational decisions is not a new concept. What is new is using AI/ML models to acquire larger datasets from operations in real time and convert the data into actionable information. These models can guide managers to optimize their operations and improve operating profit margins. In an increasingly competitive and data-driven global economy, AI/ML-enabled operational decision support can give companies a path to sustainable competitive advantages.